Fast Finite-Set Model Predictive Control for Three-Phase Four-Arm Active Front End Modular Multilevel Converters Under Unbalanced and Distorted Network Conditions

• Main Authors: Lin Qiu, Xing Liu, Jiahao Sun, Jian Zhang, Jien Ma, Youtong Fang
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Fast finite-set model predictive control (FFS-MPC); three-phase four-arm active front end modular multilevel converters (AFE-MMCs); cost function; steady-state performance
• Online Access: https://ieeexplore.ieee.org/document/8976107/

This paper focuses on a fast finite-set model predictive control (FFS-MPC) for three-phase four-arm active front end modular multilevel converters (AFE-MMCs) under unbalanced and distorted network conditions. The main aim of this paper is to enhance the steady-state performance of the whole system while remaining computationally feasible. Firstly, a novel topology, which has a good potential to improve the fault tolerance ability of MMCs, is presented in this literature. Secondly, in order to enhance the steady-state control performance, a new FFS-MPC methodology is proposed to serve this purpose. Specifically, the philosophy behind the proposed solution is to formulate a user-predefined cost function formula by embedding a power compensation term and an integral error term at the same time, which improves the power quality under normal and under abnormal conditions. However, it is important to notice that the computational complexity will be increased while applying the proposed solution to the control of three-phase four-arm AFE-MMCs. To solve this issue, a fast MPC is introduced into the proposed methodology to improve the computational efficiency, making it suitable for multilevel converters control. Finally, the effectiveness and feasibility of the proposed FFS-MPC methodology can be validated by the comprehensive results for regulated three-phase four-arm AFE-MMCs.